Characteristics Of Acoustic Signals Research Articles

Adaptive plasticity of bushcricket acoustic signalling in socially heterogeneous choruses

In many species, males produce sounds to attract a female. In a chorus, males actively adjust their acoustic production depending on the presence and number of mating rivals. Plasticity in signal features might then be advantageous as males could tune their signalling activity to the contest intensity. However, the ability to exhibit plastic behaviour might be constrained and interindividual variation in plasticity can occur. In this field study, we examined the influence of body mass and social environment on the signal expression of male bushcrickets, Poecilimon veluchianus veluchianus. We tested whether differences in signal features of rivals of different body mass and number led to changes in the competition pressure perceived by focal males and, consequently, in their acoustic responses. We used the first principal component from a principal component analysis, reflecting verse duration, duty cycle and syllable number, as a measure of acoustic output. We found that the rivals' acoustic output depended only on their number, being higher for two rivals than one. However, the response of heavy focal males depended on the rivals' body mass. Contrary to what we expected, heavy males produced shorter verses with fewer syllables when competing against two heavy rivals than when competing against one or two light rivals or one heavy rival. Facing light competitors, heavy focal males responded more to two rivals than to one. In contrast, light focal males did not vary their signal features depending on competition conditions. These results indicate that body mass and social context drive interindividual variation in the capacity of P. v. veluchianus males to adjust their signal features to competition levels. We compare these findings with those previously obtained on congeneric species and discuss the outcomes' similarities and discrepancies. Lastly, we provide suggestions for future studies of socially induced plasticity in sexually selected acoustic signal features.

An аutomatic mode control of the jet mill loading

A thin jet grinding is quite an energy-consuming process. Its production depends on the fullness of the mill chamber with material. Therefore, it is extremely important to control the loading of the jets with material and to reload the necessary portion of material in time.Previous studies have shown the effectiveness of control the jet grinding process based on acoustic monitoring. The interrelation of technological, regime and acoustic parameters of the process has been established, an automated mill control system is being developed. For each required size of the ready product, its own grinding mode and classification and, accordingly, its range of acoustic signal characteristics are set to achieve maximum grinding productivity. This is the basis for controlling the jet mill operation according to the results of acoustic monitoring.The aim of the paper is to develop an automated control system for the loading bunker of the jet mill based on the analysis of the acoustic signals of the grinding zone.The study was conducted for different recording modes of acoustic monitoring signals and the grinding process of various bulk materials. To amplify the signal, an operational amplifier with a gain of 10 was used. To eliminate the measurement error, a sample of 100 values is taken. The used equipment has a limit time for the ADC polling for each program cycle. To estimate the error, an experimental study of signals was carried out. After processing all the data, the control signal is fed to the control unit of the solenoid, which is assembled on the basis of a field-effect transistor with an operating voltage of 60 V and a maximum current is up to 100 A. Then the control signal is transmitted to the opening or closing of the loading bunker gate.The numerical experiment improved the analyzing method of the acoustic signal of the grinding zone, reduced the error. A hardware base of the control system was created and a model of a controlled loading bunker was implemented. A mill loading control system has been developed through a controlled bunker based on the results of continuous acoustic monitoring of the grinding process.

Local acoustic field enhancement of single cell photoacoustic signal detection based on metamaterial structure

The photoacoustic signal from single cell plays an important role in medical diagnosis. Currently, the single-cell photoacoustic detection has limitations. To overcome the limits, an acoustic metamaterial model based on the acoustic waveguide theory for local enhancement of the ultrahigh frequency (UHF) acoustic signals was implemented in this study and it was applied to study the acoustic signal characteristics of individual cells. The local field enhancement effect at different frequencies and the specific mechanism of the model was evaluated in the simulation experiment. This proposing acoustic metamaterial structure of good practical value can be used in various applications for acoustic signal processing.

Underwater Acoustic Target Recognition: A Combination of Multi-Dimensional Fusion Features and Modified Deep Neural Network

A method with a combination of multi-dimensional fusion features and a modified deep neural network (MFF-MDNN) is proposed to recognize underwater acoustic targets in this paper. Specifically, due to the complex and changeable underwater environment, it is difficult to describe underwater acoustic signals with a single feature. The Gammatone frequency cepstral coefficient (GFCC) and modified empirical mode decomposition (MEMD) are developed to extract multi-dimensional features in this paper. Moreover, to ensure the same time dimension, a dimension reduction method is proposed to obtain multi-dimensional fusion features in the original underwater acoustic signals. Then, to reduce redundant features and further improve recognition accuracy, the Gaussian mixture model (GMM) is used to modify the structure of a deep neural network (DNN). Finally, the proposed underwater acoustic target recognition method can obtain an accuracy of 94.3% under a maximum of 800 iterations when the dataset has underwater background noise with weak targets. Compared with other methods, the recognition results demonstrate that the proposed method has higher accuracy and strong adaptability.

Characteristics of acoustic signals from lightning using a microphone array observation system

The acoustic signals of thunder provide information on the lightning channel, such as frequency, power spectra density, and images. In this study, to analyze the frequency variation in acoustic signals of thunder and to reconstruct the 3D lightning channel imaging, the short time Fourier transform (STFT) and differential time of arrival (DTOA) methods were used. The results showed that the frequency for four microphones varied from 4.88 Hz to 175.78 Hz. Furthermore, the range of power spectra density values for the four microphones was between -0.59 dB/Hz and -28.31 dB/Hz. This system can reconstruct 3D lightning channel imaging.

Quantitative Genetic Mapping and Genome Assembly in the Lesser Wax Moth Achroia grisella

Specific characteristics of the male Achroia grisella acoustic mating signal determine a male’s attractiveness toward females. These features are genetically variable in populations, and mapping experiments have been used to identify loci contributing to song variation, and understand the evolutionary forces acting on this important sexual trait. Here we built on this foundation and carried out QTL (Quantitative Trait Locus) mapping using >1,000 recombinant individuals, genotyping this large cohort at thousands of sequence-based markers covering the entire collection of 30 A. grisella chromosomes. This dense marker set, coupled with our development of an annotated, draft genome of A. grisella, allowed us to link >3,000 genome scaffolds, >10,000 predicted genes, and close to 275Mb of genome sequence to chromosomes. Our QTL mapping confirmed a fraction of the QTL identified in a previous study, and additionally revealed novel loci. Collectively, QTL explained only small fractions of the phenotypic variance, suggesting many more causative factors remain below the detection threshold of our study. A surprising, and ultimately challenging feature of our study was the low level of intrachromosomal recombination present in our mapping population. This led to difficulty ordering markers along linkage groups, necessitating a chromosome-by-chromosome mapping approach, rather than true interval mapping, and precluded confident ordering/orienting of scaffolds along each chromosome. Nonetheless, our study increased the genomic resources available for the A. grisella system. Enabled by ever more powerful technologies, future investigators will be able to leverage our data to provide more detailed genetic dissection of male song variation in A. grisella.

Optimal design for real-time quantitative monitoring of sand in gas flowline using computational intelligence assisted design framework

Abstract Global demand for oil and gas is still increasing rapidly. The direct consequence of this is the increased operating pressure amid concerns over increasing sand production. According to the Society of Petroleum Engineers (SPE), 70% of the world's hydrocarbon reserves are contained in reservoirs situated on unconsolidated formations. Given the reality of these formations, sand production will certainly be a problem of significant concern particularly during the later life of the fields when they become more ‘mature’. However, to monitor sand and optimise its production for improved recovery and safety, life extension and economy of the fields and ensured reliability, the automatic detection and prediction of sand flow characteristic measurements; sand flow rate (SFR), sand concentration (SC), line pressure drop (PD), and gas velocity (GV), has become an important research topic of great interest. Despite this importance, discussion of the topic is still lacking in the literature. This paper proposes a novel and robust architecture of intelligent real-time sand flow characteristic measurement using an acoustic sensor and computational intelligence assisted design (CIAD) framework. It fully incorporates acoustic signal processing and analysis, prediction algorithms and optimisation algorithms in the design. Acoustic features based on acoustic signal processing techniques are extracted to reduce the dimensionality of the acoustic signals. A classical Artificial Neural Network (ANN) is used to model the non-linear relationships between the acoustic signal characteristics and the flow characteristics measurands. In addition, the ANN algorithm adapts its weights and biases using the Grey Wolf Optimiser (GWO) through minimisation of the cost function during the training phase. Preliminary results obtained on a laboratory test rig demonstrate that an acoustic sensor coupled with CIAD may provide simple and robust practical solution to the measurement problem of particle-laden gas flow characteristics in real-time.

Structural health assessment of prefabricated concrete elements using Acoustic Emission: Towards an optimized damage sensing tool

Abstract The design feasibility and structural integrity of prefabricated construction elements is assessed (concrete cantilever slab carrying thermal insulation break reinforced with steel/basalt rebars and ultra-high-performance concrete) by performing incremental bending tests. The developed damage mechanisms govern failure, therefore their identification appears crucial: concrete cracking, rebars-concrete interface debonding, rebars slippage, concrete crush under compression. However, the complex fracture cannot be effectively characterized by measuring the deterioration of the mechanical properties. In this study, Acoustic Emission (AE) is applied to highlight internal damage progress. It is found based on cumulative AE hits distribution under incremental load and considering the Kaiser and Felicity effects, damage initiation is identified. The damage severity is quantified using Load/Calm ratios. AE sources localization identifies the moment at which the rebars plastically deform; debond from concrete; slip and when concrete cracks and crushes under compression. The damage mechanisms are discriminated based on acoustic signal features distribution as damage evolves (specifically, average frequency and the ratio of rise time over amplitude). It is shown that AE appears an essential and accurate monitoring method that fully characterizes the damage on complex structures. The promising results open new horizons towards the design of fully autonomous prefabricated elements carrying a smart on-line AE sensing tool.

Vibration and acoustic signal characteristics of solid particles carried in sand-water two-phase flows

Sand particle detection in pipe flow is industrially important. Here, we developed and evaluated two non-invasive methods (vibration and acoustic) with two homotypic mutual assist sensors to measure sand and to investigate the relationship between sand particles and the vibro-acoustic signal characteristics generated by sand-wall impingement in water flow. The time-behavior statistical characteristics of sand-carrying flow were analyzed, and the sand vibro-acoustic characteristics of the frequency bands were then identified via time-frequency and zoom-FFT methods. The noise features were further verified via a mutual coherence method, and then minimized via a digital band elimination filter. The accuracy of the detected sand signals was then verified via two different sensor locations. Corresponding experiments investigated the vibration and acoustic signal characteristics of sand particles in sand-water two-phase flows. The sand content ranged from 0 to 0.24 wt.% with an interval of 0.06 wt.%. The sand size ranged from 150 to 300 μm in turbulence flow. The results show that the sand concentration with different sizes correlated well with the RMS level of both the vibration and acoustic signals. In comparing these two methods, the vibration sensor approach is not only more sensitive to the variation of sand-wall impingement but is also more sensitive to the strong background noise. However, the acoustic sensor approach with the help of an acoustic focusing device offers a better signal-to-noise ratio than the vibration sensor approach. This is more advantageous for analyzing sand feature differences. This provides an alternative approach for detecting solid particle features in water flow and lays the foundation for future work with solid detection in more complex multiphase flows with multisensory fusion methods.

The study of acoustic emission waves generated from different types of sources

Abstract This paper presents the results in the analysis of acoustic emission signals that registered during the propagation of acoustic waves in the thin metal plate. The sources are excited by the fracture of mechanical pencil lead (Hsu-Nielsen) with different hardness to model various kinds of cracks at each specific location. To research and identify different types of fracture in the sample, the analysis was performed based on the method of the wavelet decomposition in this research. These results can be used to establish relation between the characteristics of acoustic signals and the parameter of the fracture of structure in the material.

Statistical Characterization of Acoustic Signals Using 1D Wavelet Transforms with Applications in Acoustical Oceanography

The paper summarizes the research carried out at the University of Crete and the Foundation for Research and Technology-HELLAS aiming at the statistical characterization of underwater acoustic signals and their subsequent use for geoacoustic inversions and applications in ocean acoustic tomography. In these applications, an acoustic signal recorded in the marine environment due to some source is used as the carrier of information on the physical parameters of the environment. Statistical characterization of acoustic signals is a pre-processing technique aiming at the definition of signal observables, to be used as input data of appropriately defined inverse problems aiming at the estimation of critical parameters of the marine environment. The statistical characterization scheme was introduced as a way to define signal observables especially in cases that typical observables such as ray arrivals or modal arrivals cannot be identified in the recorded signals. Moreover, the setting of the associated inverse problem requires just a single recording device, which renders its application practically and relatively cheap in comparison with signal inversion methods requiring reception at an array of hydrophones. The characterization scheme is based on a wavelet transform of the signal at various levels, followed by the statistical description of the wavelet sub-band coefficients. It is shown that A-stable symmetric distributions are capable of defining the statistics of these coefficients, the characteristic parameters of which are the observables of the signal to be exploited for the inversions. As the inverse problems associated with the sought applications are formulated as optimization processes, an objective function to be used as a similarity measure is defined, which in the case of the statistical characterization method is the Kullback–Leibrer Divergence (KLD), capable of comparing probability density functions. The inversion processes are performed by means of neural networks or genetic algorithms, and the performance of the combined signal characterization and inversion method has been tested with simulated and real data. It is shown, that the method works well especially with noise-free or denoised signals.

RSHSC-Routing Algorithm Based on Simplified Harmony Search and Coding for UWSNs

With the development of wireless networks and increasingly interest of people in underwater resources and environment, UWSNs are being paid more and more attention. Because of the characteristics of underwater channel and acoustic signal, the protocols used in the terrestrial networks cannot be directly used in UWSNs. In this paper, a reliable and energy-efficient routing protocol based on SHS and coding, called RSHSC, is proposed. Firstly, regular nodes are assigned to cluster heads according to simplified harmony search algorithm. Secondly, partial network encoding is introduced and the next two-hop information is considered when data packets are transmitted to sink nodes from the source node. Only the best next-hop forwards data packets. All data packets from neighbor nodes are used for decoding. Thirdly, two schemes of updating routing are designed and compared. Lastly, extensive simulations prove RSHSC is effective in improving reliability and decreasing energy consumption.

Sound Categories: Category Formation and Evidence-Based Taxonomies.

Five evidence-based taxonomies of everyday sounds frequently reported in the soundscape literature have been generated. An online sorting and category-labeling method that elicits rather than prescribes descriptive words was used. A total of N = 242 participants took part. The main categories of the soundscape taxonomy were people, nature, and manmade, with each dividing into further categories. Sounds within the nature and manmade categories, and two further individual sound sources, dogs, and engines, were explored further by repeating the procedure using multiple exemplars. By generating multidimensional spaces containing both sounds and the spontaneously generated descriptive words the procedure allows for the interpretation of the psychological dimensions along which sounds are organized. This reveals how category formation is based upon different cues – sound source-event identification, subjective-states, and explicit assessment of the acoustic signal – in different contexts. At higher levels of the taxonomy the majority of words described sound source-events. In contrast, when categorizing dog sounds a greater proportion of the words described subjective-states, and valence and arousal scores of these words correlated with their coordinates along the first two dimensions of the data. This is consistent with valence and arousal judgments being the primary categorization strategy used for dog sounds. In contrast, when categorizing engine sounds a greater proportion of the words explicitly described the acoustic signal. The coordinates of sounds along the first two dimensions were found to correlate with fluctuation strength and sharpness, consistent with explicit assessment of acoustic signal features underlying category formation for engine sounds. By eliciting descriptive words the method makes explicit the subjective meaning of these judgments based upon valence and arousal and acoustic properties, and the results demonstrate distinct strategies being spontaneously used to categorize different types of sounds.

A flexible high directivity beamformer with spherical microphone arrays.

The maximum directivity (MD) beamformer with spherical microphone arrays has many salient features in processing broadband acoustic and speech signals while suppressing noise and reverberation; but it is sensitive to sensors' self-noise and mismatch among these sensors. One effective way to deal with this sensitivity is by increasing the number of microphones, thereby improving the so-called white noise gain (WNG), but this increase may lead to many other design issues in terms of cost, array aperture, and possibly other performance degradation. This paper is tackling this sensitivity problem and presents a flexible high directivity (HD) beamforming algorithm. By approximating the ideal directivity pattern and the beamformer's beampattern with spherical harmonic series, the relationship between the two is obtained. This relationship is subsequently used to deduce a flexible HD beamformer, which can improve the WNG while achieving a directivity factor (DF) between the DF of an Nth-order MD beamformer and that of an (N-1)th-order MD one. Also derived is the analytical link between the DF and a tuning parameter and the link between the WNG and this parameter. Based on these links, one can easily determine the optimal value of the tuning parameter once the value of the DF or the WNG is specified.

Computational Approach to Musical Consonance and Dissonance.

In sixth century BC, Pythagoras discovered the mathematical foundation of musical consonance and dissonance. When auditory frequencies in small-integer ratios are combined, the result is a harmonious perception. In contrast, most frequency combinations result in audible, off-centered by-products labeled “beating” or “roughness;” these are reported by most listeners to sound dissonant. In this paper, we consider second-order beats, a kind of beating recognized as a product of neural processing, and demonstrate that the data-driven approach of Recurrence Quantification Analysis (RQA) allows for the reconstruction of the order in which interval ratios are ranked in music theory and harmony. We take advantage of computer-generated sounds containing all intervals over the span of an octave. To visualize second-order beats, we use a glissando from the unison to the octave. This procedure produces a profile of recurrence values that correspond to subsequent epochs along the original signal. We find that the higher recurrence peaks exactly match the epochs corresponding to just intonation frequency ratios. This result indicates a link between consonance and the dynamical features of the signal. Our findings integrate a new element into the existing theoretical models of consonance, thus providing a computational account of consonance in terms of dynamical systems theory. Finally, as it considers general features of acoustic signals, the present approach demonstrates a universal aspect of consonance and dissonance perception and provides a simple mathematical tool that could serve as a common framework for further neuro-psychological and music theory research.

ИССЛЕДОВАНИЕ ВОЗМОЖНОСТИ ОЦЕНКИ СОСТОЯНИЯ СТАЛЬНЫХ ОБРАЗЦОВ С ПРИМЕНЕНИЕМ ДИНАМИЧЕСКИХ СПЕКТРОГРАММ АКУСТИЧЕСКИХ СИГНАЛОВ

The main purpose of this work was to study the possibility of estimating the changes in properties of bearing steel during heat treatment by visualization using dynamic spectrograms (sonograms) that display changes in the spectral characteristics of acoustic signals within time. The changes of spectral characteristics of the point impact acoustic response in the steel specimens were studied depending on the microstructure changes generated in the specimens using heat treatment with various parameters. The analysis and comparison of acoustic response signals were performed using software spectrum analyzer. The informativeness of the applied technique aimed at estimating changes in the microstructure of steel as a result of heat treatment is shown. The analysis of dynamic spectrograms allowed revealing indicative spectral features, such as shifting in the resonant frequencies and change in the damping decrement. A clear dependence of the shift of the resonant frequencies and the damping decrement of the signal on the tempering temperature of the quenched ball-bearing steel specimens was found. After the tempered specimens were re-quenched, reverse changes in the resonant harmonics frequencies and the damping decrement were observed. Changes in the observed indicative spectral characteristics correspond to changes in hardness and microstructure of the steel. This technique can be used for development of nondestructive testing express methods to evaluate changes in the state of steel structural elements and perform quality control during heat treatment.

Deep Learning Methods for Underwater Target Feature Extraction and Recognition.

The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM) was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved.

Distorting temporal fine structure by phase shifting and its effects on speech intelligibility and neural phase locking

Envelope (E) and temporal fine structure (TFS) are important features of acoustic signals and their corresponding perceptual function has been investigated with various listening tasks. To further understand the underlying neural processing of TFS, experiments in humans and animals were conducted to demonstrate the effects of modifying the TFS in natural speech sentences on both speech recognition and neural coding. The TFS of natural speech sentences was modified by distorting the phase and maintaining the magnitude. Speech intelligibility was then tested for normal-hearing listeners using the intact and reconstructed sentences presented in quiet and against background noise. Sentences with modified TFS were then used to evoke neural activity in auditory neurons of the inferior colliculus in guinea pigs. Our study demonstrated that speech intelligibility in humans relied on the periodic cues of speech TFS in both quiet and noisy listening conditions. Furthermore, recordings of neural activity from the guinea pig inferior colliculus have shown that individual auditory neurons exhibit phase locking patterns to the periodic cues of speech TFS that disappear when reconstructed sounds do not show periodic patterns anymore. Thus, the periodic cues of TFS are essential for speech intelligibility and are encoded in auditory neurons by phase locking.

Uniaxial experimental study of the acoustic emission and deformation behavior of composite rock based on 3D digital image correlation (DIC)

In this paper, uniaxial compression tests were carried out on a series of composite rock specimens with different dip angles, which were made from two types of rock-like material with different strength. The acoustic emission technique was used to monitor the acoustic signal characteristics of composite rock specimens during the entire loading process. At the same time, an optical non-contact 3D digital image correlation technique was used to study the evolution of axial strain field and the maximal strain field before and after the peak strength at different stress levels during the loading process. The effect of bedding plane inclination on the deformation and strength during uniaxial loading was analyzed. The methods of solving the elastic constants of hard and weak rock were described. The damage evolution process, deformation and failure mechanism, and failure mode during uniaxial loading were fully determined. The experimental results show that the $$\theta = 0{^{\circ }}$$ – $$45{^{\circ }}$$ specimens had obvious plastic deformation during loading, and the brittleness of the $$\theta = 60{^{\circ }}$$ – $$90{^{\circ }}$$ specimens gradually increased during the loading process. When the anisotropic angle $$\theta $$ increased from $$0{^{\circ }}$$ to $$90{^{\circ }}$$ , the peak strength, peak strain, and apparent elastic modulus all decreased initially and then increased. The failure mode of the composite rock specimen during uniaxial loading can be divided into three categories: tensile fracture across the discontinuities ( $$\theta = 0{^{\circ }}$$ – $$30{^{\circ }})$$ , sliding failure along the discontinuities ( $$\theta = 45{^{\circ }}$$ – $$75{^{\circ }})$$ , and tensile-split along the discontinuities ( $$\theta = 90{^{\circ }})$$ . The axial strain of the weak and hard rock layers in the composite rock specimen during the loading process was significantly different from that of the $$\theta = 0{^{\circ }}$$ – $$45{^{\circ }}$$ specimens and was almost the same as that of the $$\theta = 60{^{\circ }}$$ – $$90{^{\circ }}$$ specimens. As for the strain localization highlighted in the maximum principal strain field, the $$\theta = 0{^{\circ }}$$ – $$30{^{\circ }}$$ specimens appeared in the rock matrix approximately parallel to the loading direction, while in the $$\theta = 45{^{\circ }}$$ – $$90{^{\circ }}$$ specimens it appeared at the hard and weak rock layer interface.

Leak detection of water distribution pipeline subject to failure of socket joint based on acoustic emission and pattern recognition

Early leak detection is of great importance for life-cycle maintenance and management of municipal pipeline system. Due to economic and technical efficiency, ductile iron pipe segments and socket joints are widely used in practice to construct water distribution systems. The ductile configuration of the socket joint allowing for large deformation constitutes the most common cause for water leakage. Using acoustic emission (AE) techniques, this paper presents an experimental study on leak detection of a water distribution system subject to failure of socket joint. The acoustic characteristics of leak signals in the socket and spigot pipe segments are investigated. After feature extraction and selection, a classifier based on artificial neural network (ANN) is established. It has been validated that the dominant frequencies of the AE leak signals due to the failure of the socket joint concentrate on 0–10 kHz. The proposed ANN-based method can achieve good estimation accuracy of 97.2% and 96.9% by using the feature set {Peak, Mean, Peak Frequency, Kurtosis} and {Mean, Peak Frequency}.